Generative AI Architect

Overview

We are seeking a highly skilled and experienced

Azure OpenAI Architect

to lead the design and implementation of end-to-end Generative AI systems. This role goes beyond building AI models—it involves architecting robust, scalable, and secure AI-powered applications and services across the full technology stack. The ideal candidate will combine deep expertise in AI / ML, modern software engineering practices, and cloud architecture to deliver impactful business solutions using Azure OpenAI and related technologies.

You will play a pivotal role in guiding technical strategy, integrating LLMs, overseeing system design and development, and ensuring seamless deployment and operation of AI-powered applications.

Task and Responsibilities :

Architect End-to-End AI Solutions : Lead the design of comprehensive AI systems from data ingestion and model orchestration to full-stack applications and secure deployment pipelines.

Cloud Architecture & Infrastructure : Design and manage scalable Azure cloud infrastructure, including networking, resource provisioning, monitoring, and cost optimization.

Security & Identity Management : Implement secure architectures using Azure Active Directory, Managed Identities, Role-Based Access Control (RBAC), Key Vault, and compliance with organizational and regulatory standards.

Gen AI & LLM Integration : Design and deploy Generative AI applications using Azure OpenAI Service, Open Source LLMs (e.g., Llama2, Mistral), and frameworks like LangChain, Semantic Kernel, and vector databases.

Software Engineering : Develop full-stack AI applications using Python (FastAPI) for backend services and ReactJS for user interfaces.

DevOps & CI / CD : Automate infrastructure and deployment workflows using Azure DevOps, GitHub Actions, or Terraform for continuous integration, delivery, and monitoring.

Data Engineering : Build and manage data pipelines for ingesting, processing, and enriching structured and unstructured data using services like Azure Data Factory, Azure Synapse, and Azure AI Document Intelligence.

Search & RAG Implementation : Implement advanced search features and Retrieval-Augmented Generation (RAG) pipelines using Azure AI Search, vector stores, embeddings, summarization, and query transformation.

Monitoring & Optimization : Continuously monitor solution performance and availability, and optimize AI workloads across the stack.

Collaboration & Leadership : Work with data scientists, engineers, product managers, and business stakeholders to align solutions with organizational goals.

Innovation & Best Practices : Stay ahead of AI trends and Azure capabilities, promoting best practices in architecture, coding, and system design.

Qualifications : 8+ years

of experience in IT, with

5+ years

focused on AI / ML engineering or AI system architecture.

Proven track record in designing and delivering

end-to-end AI / GenAI solutions

in production environments.

Strong proficiency with

Azure Cloud Platform , including services like :

Azure OpenAI, Azure AI Search, Azure App Services, Azure Functions, Azure Key Vault

Azure Cognitive Services (Speech, Vision, Document Intelligence)

Azure Kubernetes Service (AKS), Azure Container Apps

Deep understanding of

cloud networking, security, and identity / access management

on Azure.

Hands-on experience with

DevOps tools

(Azure DevOps, GitHub Actions, Terraform, Bicep).

Expertise in

backend development

using

Python (FastAPI, Flask)

and

frontend development

using

ReactJS .

Familiarity with

LLMs, Transformer architectures , vector search, LangChain, and RAG pipelines.

Experience with

vector databases

(e.g., Pinecone, Weaviate, FAISS, Qdrant) and semantic search techniques.

Strong skills in

data engineering , working with structured / unstructured data pipelines and ETL tools.

Relevant certifications preferred (e.g., Microsoft Certified : Azure Solutions Architect Expert, Azure AI Engineer Associate).

Excellent analytical, problem-solving, and communication skills.

Passion for innovation and a track record of delivering enterprise-grade AI solutions.

Nice To Have : Experience with

Kubernetes ,

MLflow , or

Ray

for distributed training / deployment.

Familiarity with

SaaS AI product development

or

MLOps pipelines .

Exposure to

multi-modal GenAI

(text, image, audio, video).